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Quantitative identification of technological paradigm changes using knowledge persistence.

Changbae Mun1, Sejun Yoon1, Yongmin Kim1

  • 1Department of Information Systems, Hanyang University, Seoul, Republic of Korea.

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Summary

This study introduces a novel method using patent citation networks to identify past technological paradigms and predict future ones. It analyzes knowledge persistence to pinpoint paradigm shifts and emerging innovations in technology evolution.

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Area of Science:

  • Bibliometrics
  • Innovation Studies
  • Intellectual Property Analysis

Background:

  • Technological paradigms represent critical knowledge shifts within specific historical periods.
  • Identifying these paradigm shifts is crucial for understanding and predicting technological evolution.
  • Existing methods often lack quantitative approaches to track dynamic changes in technological importance.

Purpose of the Study:

  • To propose and validate a quantitative method for identifying past technological paradigms and predicting future ones.
  • To leverage patent citation networks and knowledge persistence for analyzing technological evolution.
  • To demonstrate the method's efficacy through a case study in genome sequencing technology.

Main Methods:

  • Development of a method analyzing patent citation networks to quantify knowledge persistence.
  • Identification of paradigmatic inventions based on high and rapidly increasing knowledge persistence.
  • Prediction of future paradigms by identifying patents exhibiting similar increasing trends to historical paradigms.

Main Results:

  • The method successfully identified historically recognized critical inventions as past paradigms in genome sequencing.
  • Emerging technologies with significant knowledge persistence trends were identified as potential future paradigms.
  • The study demonstrated a quantitative approach to track paradigm shifts and forecast technological trajectories.

Conclusions:

  • The proposed method provides a robust framework for quantitatively identifying and predicting technological paradigms.
  • Knowledge persistence analysis in patent networks offers valuable insights into the dynamics of technological innovation.
  • This approach can aid researchers and policymakers in understanding and navigating future technological landscapes.